Use CPU devices if CUDA is not available.

app.py

@@ -13,11 +13,12 @@ from cldm.ddim_hacked import DDIMSampler
 from mediapipe_face_common import generate_annotation
 
 # Download the SD 1.5 model from HF
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model_path = hf_hub_download(repo_id="CrucibleAI/ControlNetMediaPipeFace", filename="models/controlnet_sd21_laion_face_v2_full.ckpt", repo_type="model")
 config_path = hf_hub_download(repo_id="CrucibleAI/ControlNetMediaPipeFace", filename="models/cldm_v21.yaml", repo_type="model")
 model = create_model(config_path).cpu()
-model.load_state_dict(load_state_dict(model_path, location='cuda'))
-model = model.cuda()
+model.load_state_dict(load_state_dict(model_path, location=device))
+model = model.to(device)
 ddim_sampler = DDIMSampler(model)  # ControlNet _only_ works with DDIM.
 
 
@@ -27,7 +28,7 @@ def process(input_image: Image.Image, prompt, a_prompt, n_prompt, max_faces: int
         visualization = Image.fromarray(empty)  # Save to help debug.
 
         empty = numpy.moveaxis(empty, 2, 0)  # h, w, c -> c, h, w
-        control = torch.from_numpy(empty.copy()).float().cuda() / 255.0
+        control = torch.from_numpy(empty.copy()).float().to(device) / 255.0
         control = torch.stack([control for _ in range(num_samples)], dim=0)
         # control = einops.rearrange(control, 'b h w c -> b c h w').clone()